Search

Invasive species in monocultures in introduced habitats experience different competitive conditions compared with their native habitats. Invasive monospecific stands can be composed of highly related individuals, creating high opportunity for kin selection in invaded habitats. We investigated the responses of North American and European ecotypes of the invasive species garlic mustard ( Alliaria petiolata (M. Bieb.) Cavara & Grande) to aspects of the competitive environment including density, presence and identity of conspecific neighbors. Several aboveground morphological and performance traits responded to density independent of the origin of the plants. Belowground, however, North American ecotypes allocated more resources to roots and particularly to the taproot portion of their root system, while petiole elongation was density-dependent with North American ecotypes showing reduced elongation in high density compared to European ecotypes. These results were consistent with the evolution of reduced competitive ability in garlic mustard and indicated better resource storage in the introduced environment. Differential responses to neighbour identity indicated the ability for kin recognition in this species. Thus, a combination of natural and kin selection favouring better resource storage and less intraspecific competition, aided by the ability for kin recognition, may be responsible for the success of garlic mustard as an invasive species in North America.

In this study, an automatic relevance determination-based Bayesian neural network (ARD-BNN) approach is employed combing hydro-geochemical and geo-electrical information to predict groundwater quality index (GWQI) of coastal Maharashtra, India. In the first step, to incorporate geo-electrical information for GWQI forward computation, the weight corresponding to true resistivity of the earth layer is estimated via ARD-BNN modelling using training sample generated for groundwater classification. In the second step, incorporating earth resistivity weight information, a total of 1500 training samples are created honouring World Health Organisation (WHO) guidelines for GWQI prediction. Prior to actual data analysis, we explored the algorithm on GWQI variable series assorted with different level of complex (red) noise to examine the bounds of network hyper-parameter. What distinguishes our approach from previous approach for ARD-BNN optimization is that we seek to develop a mechanism which allows specific weight estimation and provides insight into which hyper parameters and their bounds are appropriate to predict GWQI from noise intervened GWQI data. The model shows excellent performance between predicted and computed GWQI both in training (trn) and test (tst) data with Pearson’s correlation coefficient (rtrn ~ 0.91 and rtst ~ 0.90), root-mean-squared-error (RMSE) error (RMSEtrn ~ 1.2 and RMSEtst ~ 1.4), reduction of error (RE) (REtrn ~ 0.98 and REtst ~ 0.97), and index of agreement (IA) (IAtrn ~ 0.95 and IAtst ~ 0.93). Red noise analysis shows that the ARD-BNN model is robust up to the noise level of 20% in the input variable for GWQI prediction. The network adopted relevance analysis to indicate the relative importance of input parameter in the prediction of GWQI via ARD-BNN modelling which showed that chloride [Cl−], [pH], [HCO3 −] and sodium [Na+] are dominant while considering various simulations for characterizing the GWQI of coastal Maharashtra. The approach used here, could be useful in understanding the relative contribution and/or modelling pollution source in many other environmental applications.